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While deep learning systems have gained significant ground in speechenhancement research, these systems have yet to make use of the full potentialof deep learning systems to provide high-level feedback. In particular,phonetic feedback is rare in speech enhancement research even though itincludes valuable top-down information. We use the technique of mimic loss toprovide phonetic feedback to an off-the-shelf enhancement system, and findgains in objective intelligibility scores on CHiME-4 data. This technique takesa frozen acoustic model trained on clean speech to provide valuable feedback tothe enhancement model, even in the case where no parallel speech data isavailable. Our work is one of the first to show intelligibility improvement forneural enhancement systems without parallel speech data, and we show phoneticfeedback can improve a state-of-the-art neural enhancement system trained withparallel speech data.

Phonetic Feedback for Speech Enhancement With and Without Parallel Speech Data